Machine Learning-based Early Clinical Warning of High-risk Patients

• Sponsor: Southeast University, China (Other)
• Overall Status: Not yet recruiting
• CT.gov ID: NCT05410171
• Collaborator: (none)
• Enrollment: 1,000
• Arms: 2
• Anticipated Duration (Months): 18

Study Details

Study Description

Brief Summary

Through the early warning platform for inpatients established by our hospital, the various indicators of patients collected in real time are carried out for automated intelligent evaluation and analysis, early warning of high-risk patients to assess the impact on patient prognosis and the impact on the occurrence of adverse events in inpatients.

Condition or Disease	Intervention/Treatment	Phase
Early Warning	Device: early warning platform	N/A

Study Design

• Study Type: Interventional
• Anticipated Enrollment: 1000 participants
• Allocation: Non-Randomized
• Intervention Model: Sequential Assignment
• Masking: None (Open Label)
• Primary Purpose: Health Services Research
• Official Title: Machine Learning-based Early Clinical Warning of High-risk Patients
• Anticipated Study Start Date: Jun 1, 2022
• Anticipated Primary Completion Date: Jun 1, 2023
• Anticipated Study Completion Date: Dec 1, 2023

Arms and Interventions

Arm	Intervention/Treatment
Experimental: AI group patients evaluated by early warning platform	Device: early warning platform; High risk inpatients will be evaluated by early warning platform
No Intervention: usual care group patients not evaluated by early warning platform

Outcome Measures

Primary Outcome Measures

1. hospital mortality [30day]

Eligibility Criteria

Criteria

• Ages Eligible for Study: 18 Years to 80 Years
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No

Inclusion Criteria:

1. Patients who use ECG monitoring

2. Age ≥ 18 years old

3. Understand and sign an informed consent form

Exclusion Criteria:

• Pregnancy or lactation

Contacts and Locations

Locations

No locations specified.

Sponsors and Collaborators

• Southeast University, China

Investigators

None specified.

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• Responsible Party: Songqiao Liu, Head of Information Division, Southeast University, China
• ClinicalTrials.gov Identifier: NCT05410171
• Other Study ID Numbers: 2021ZDSYLL346-P01
• First Posted: Jun 8, 2022
• Last Update Posted: Jun 8, 2022
• Last Verified: Jun 1, 2022

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No

Study Results

No Results Posted as of Jun 8, 2022